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06/06/2019
Unsupervised Co-Learning on G-Manifolds Across Irreducible Representations
We introduce a novel co-learning paradigm for manifolds naturally equipp...
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05/31/2019
Representation Theoretic Patterns in Multi-Frequency Class Averaging for Three-Dimensional Cryo-Electron Microscopy
We develop in this paper a novel intrinsic classification algorithm -- m...
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05/23/2019
SelectNet: Learning to Sample from the Wild for Imbalanced Data Training
Supervised learning from training data with imbalanced class sizes, a co...
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02/02/2019
Uniform-in-Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation
Diffusion approximation provides weak approximation for stochastic gradi...
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01/24/2019
Multi-Frequency Phase Synchronization
We propose a novel formulation for phase synchronization -- the statisti...
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12/18/2018
Semi-Riemannian Manifold Optimization
We introduce in this paper a manifold optimization framework that utiliz...
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09/06/2018
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds
Inspired by recent interests of developing machine learning and data min...
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07/31/2018
Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics
We demonstrate applications of the Gaussian process-based landmarking al...
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03/22/2018
Curvature of Hypergraphs via Multi-Marginal Optimal Transport
We introduce a novel definition of curvature for hypergraphs, a natural ...
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02/09/2018
Gaussian Process Landmarking on Manifolds
As a means of improving analysis of biological shapes, we propose a gree...
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06/04/2016
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets
We propose an image representation scheme combining the local and nonloc...
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03/17/2015